# --------------------------------------------------------
# TFFRCNN - Resnet50
# Copyright (c) 2016
# Licensed under The MIT License [see LICENSE for details]
# Written by miraclebiu
# --------------------------------------------------------
import tensorflow as tf
from .network import Network
import pdb
from ..fast_rcnn.config import cfg

class Resnet101_test(Network):
    def __init__(self, trainable=True):
        self.inputs = []
        self.data = tf.placeholder(tf.float32, shape=[None, None, None, 3])
        self.im_info = tf.placeholder(tf.float32, shape=[None, 3])
        self.keep_prob = tf.placeholder(tf.float32)
        self.layers = dict({'data':self.data, 'im_info':self.im_info})
        self.trainable = trainable
        self.setup()

    def setup(self):

        n_classes = cfg.NCLASSES
        # anchor_scales = [8, 16, 32]
        anchor_scales = cfg.ANCHOR_SCALES
        _feat_stride = [16, ]

        (self.feed('data')
             .conv(7, 7, 64, 2, 2, biased=False, relu=False, name='conv1')
             .batch_normalization(relu=True, name='bn_conv1', is_training=False)
             .max_pool(3, 3, 2, 2, padding='VALID',name='pool1')
             .conv(1, 1, 256, 1, 1, biased=False, relu=False, name='res2a_branch1')
             .batch_normalization(name='bn2a_branch1',is_training=False,relu=False))

        (self.feed('pool1')
             .conv(1, 1, 64, 1, 1, biased=False, relu=False, name='res2a_branch2a')
             .batch_normalization(relu=True, name='bn2a_branch2a',is_training=False)
             .conv(3, 3, 64, 1, 1, biased=False, relu=False, name='res2a_branch2b')
             .batch_normalization(relu=True, name='bn2a_branch2b',is_training=False)
             .conv(1, 1, 256, 1, 1, biased=False, relu=False, name='res2a_branch2c')
             .batch_normalization(name='bn2a_branch2c',is_training=False,relu=False))

        (self.feed('bn2a_branch1', 
                   'bn2a_branch2c')
             .add(name='res2a')
             .relu(name='res2a_relu')
             .conv(1, 1, 64, 1, 1, biased=False, relu=False, name='res2b_branch2a')
             .batch_normalization(relu=True, name='bn2b_branch2a',is_training=False)
             .conv(3, 3, 64, 1, 1, biased=False, relu=False, name='res2b_branch2b')
             .batch_normalization(relu=True, name='bn2b_branch2b',is_training=False)
             .conv(1, 1, 256, 1, 1, biased=False, relu=False, name='res2b_branch2c')
             .batch_normalization(name='bn2b_branch2c',is_training=False,relu=False))

        (self.feed('res2a_relu', 
                   'bn2b_branch2c')
             .add(name='res2b')
             .relu(name='res2b_relu')
             .conv(1, 1, 64, 1, 1, biased=False, relu=False, name='res2c_branch2a')
             .batch_normalization(relu=True, name='bn2c_branch2a',is_training=False)
             .conv(3, 3, 64, 1, 1, biased=False, relu=False, name='res2c_branch2b')
             .batch_normalization(relu=True, name='bn2c_branch2b',is_training=False)
             .conv(1, 1, 256, 1, 1, biased=False, relu=False, name='res2c_branch2c')
             .batch_normalization(name='bn2c_branch2c',is_training=False,relu=False))

        (self.feed('res2b_relu', 
                   'bn2c_branch2c')
             .add(name='res2c')
             .relu(name='res2c_relu')
             .conv(1, 1, 512, 2, 2, biased=False, relu=False, name='res3a_branch1',padding='VALID')
             .batch_normalization(name='bn3a_branch1',is_training=False,relu=False))

        (self.feed('res2c_relu')
             .conv(1, 1, 128, 2, 2, biased=False, relu=False, name='res3a_branch2a',padding='VALID')
             .batch_normalization(relu=True, name='bn3a_branch2a',is_training=False)
             .conv(3, 3, 128, 1, 1, biased=False, relu=False, name='res3a_branch2b')
             .batch_normalization(relu=True, name='bn3a_branch2b',is_training=False)
             .conv(1, 1, 512, 1, 1, biased=False, relu=False, name='res3a_branch2c')
             .batch_normalization(name='bn3a_branch2c',is_training=False,relu=False))

        (self.feed('bn3a_branch1', 
                   'bn3a_branch2c')
             .add(name='res3a')
             .relu(name='res3a_relu')
             .conv(1, 1, 128, 1, 1, biased=False, relu=False, name='res3b1_branch2a')
             .batch_normalization(relu=True, name='bn3b1_branch2a',is_training=False)
             .conv(3, 3, 128, 1, 1, biased=False, relu=False, name='res3b1_branch2b')
             .batch_normalization(relu=True, name='bn3b1_branch2b',is_training=False)
             .conv(1, 1, 512, 1, 1, biased=False, relu=False, name='res3b1_branch2c')
             .batch_normalization(name='bn3b1_branch2c',is_training=False,relu=False))

        (self.feed('res3a_relu', 
                   'bn3b1_branch2c')
             .add(name='res3b1')
             .relu(name='res3b1_relu')
             .conv(1, 1, 128, 1, 1, biased=False, relu=False, name='res3b2_branch2a')
             .batch_normalization(relu=True, name='bn3b2_branch2a',is_training=False)
             .conv(3, 3, 128, 1, 1, biased=False, relu=False, name='res3b2_branch2b')
             .batch_normalization(relu=True, name='bn3b2_branch2b',is_training=False)
             .conv(1, 1, 512, 1, 1, biased=False, relu=False, name='res3b2_branch2c')
             .batch_normalization(name='bn3b2_branch2c',is_training=False,relu=False))

        (self.feed('res3b1_relu', 
                   'bn3b2_branch2c')
             .add(name='res3b2')
             .relu(name='res3b2_relu')
             .conv(1, 1, 128, 1, 1, biased=False, relu=False, name='res3b3_branch2a')
             .batch_normalization(relu=True, name='bn3b3_branch2a',is_training=False)
             .conv(3, 3, 128, 1, 1, biased=False, relu=False, name='res3b3_branch2b')
             .batch_normalization(relu=True, name='bn3b3_branch2b',is_training=False)
             .conv(1, 1, 512, 1, 1, biased=False, relu=False, name='res3b3_branch2c')
             .batch_normalization(name='bn3b3_branch2c',is_training=False,relu=False))

        (self.feed('res3b2_relu', 
                   'bn3b3_branch2c')
             .add(name='res3b3')
             .relu(name='res3b3_relu')
             .conv(1, 1, 1024, 2, 2, biased=False, relu=False, name='res4a_branch1',padding='VALID')
             .batch_normalization(name='bn4a_branch1',is_training=False,relu=False))

        (self.feed('res3b3_relu')
             .conv(1, 1, 256, 2, 2, biased=False, relu=False, name='res4a_branch2a',padding='VALID')
             .batch_normalization(relu=True, name='bn4a_branch2a',is_training=False)
             .conv(3, 3, 256, 1, 1, biased=False, relu=False, name='res4a_branch2b')
             .batch_normalization(relu=True, name='bn4a_branch2b',is_training=False)
             .conv(1, 1, 1024, 1, 1, biased=False, relu=False, name='res4a_branch2c')
             .batch_normalization(name='bn4a_branch2c',is_training=False,relu=False))

        (self.feed('bn4a_branch1', 
                   'bn4a_branch2c')
             .add(name='res4a')
             .relu(name='res4a_relu')
             .conv(1, 1, 256, 1, 1, biased=False, relu=False, name='res4b1_branch2a')
             .batch_normalization(relu=True, name='bn4b1_branch2a',is_training=False)
             .conv(3, 3, 256, 1, 1, biased=False, relu=False, name='res4b1_branch2b')
             .batch_normalization(relu=True, name='bn4b1_branch2b',is_training=False)
             .conv(1, 1, 1024, 1, 1, biased=False, relu=False, name='res4b1_branch2c')
             .batch_normalization(name='bn4b1_branch2c',is_training=False,relu=False))

        (self.feed('res4a_relu', 
                   'bn4b1_branch2c')
             .add(name='res4b1')
             .relu(name='res4b1_relu')
             .conv(1, 1, 256, 1, 1, biased=False, relu=False, name='res4b2_branch2a')
             .batch_normalization(relu=True, name='bn4b2_branch2a',is_training=False)
             .conv(3, 3, 256, 1, 1, biased=False, relu=False, name='res4b2_branch2b')
             .batch_normalization(relu=True, name='bn4b2_branch2b',is_training=False)
             .conv(1, 1, 1024, 1, 1, biased=False, relu=False, name='res4b2_branch2c')
             .batch_normalization(name='bn4b2_branch2c',is_training=False,relu=False))

        (self.feed('res4b1_relu', 
                   'bn4b2_branch2c')
             .add(name='res4b2')
             .relu(name='res4b2_relu')
             .conv(1, 1, 256, 1, 1, biased=False, relu=False, name='res4b3_branch2a')
             .batch_normalization(relu=True, name='bn4b3_branch2a',is_training=False)
             .conv(3, 3, 256, 1, 1, biased=False, relu=False, name='res4b3_branch2b')
             .batch_normalization(relu=True, name='bn4b3_branch2b',is_training=False)
             .conv(1, 1, 1024, 1, 1, biased=False, relu=False, name='res4b3_branch2c')
             .batch_normalization(name='bn4b3_branch2c',is_training=False,relu=False))

        (self.feed('res4b2_relu', 
                   'bn4b3_branch2c')
             .add(name='res4b3')
             .relu(name='res4b3_relu')
             .conv(1, 1, 256, 1, 1, biased=False, relu=False, name='res4b4_branch2a')
             .batch_normalization(relu=True, name='bn4b4_branch2a',is_training=False)
             .conv(3, 3, 256, 1, 1, biased=False, relu=False, name='res4b4_branch2b')
             .batch_normalization(relu=True, name='bn4b4_branch2b',is_training=False)
             .conv(1, 1, 1024, 1, 1, biased=False, relu=False, name='res4b4_branch2c')
             .batch_normalization(name='bn4b4_branch2c',is_training=False,relu=False))

        (self.feed('res4b3_relu', 
                   'bn4b4_branch2c')
             .add(name='res4b4')
             .relu(name='res4b4_relu')
             .conv(1, 1, 256, 1, 1, biased=False, relu=False, name='res4b5_branch2a')
             .batch_normalization(relu=True, name='bn4b5_branch2a',is_training=False)
             .conv(3, 3, 256, 1, 1, biased=False, relu=False, name='res4b5_branch2b')
             .batch_normalization(relu=True, name='bn4b5_branch2b',is_training=False)
             .conv(1, 1, 1024, 1, 1, biased=False, relu=False, name='res4b5_branch2c')
             .batch_normalization(name='bn4b5_branch2c',is_training=False,relu=False))

        (self.feed('res4b4_relu', 
                   'bn4b5_branch2c')
             .add(name='res4b5')
             .relu(name='res4b5_relu')
             .conv(1, 1, 256, 1, 1, biased=False, relu=False, name='res4b6_branch2a')
             .batch_normalization(relu=True, name='bn4b6_branch2a',is_training=False)
             .conv(3, 3, 256, 1, 1, biased=False, relu=False, name='res4b6_branch2b')
             .batch_normalization(relu=True, name='bn4b6_branch2b',is_training=False)
             .conv(1, 1, 1024, 1, 1, biased=False, relu=False, name='res4b6_branch2c')
             .batch_normalization(name='bn4b6_branch2c',is_training=False,relu=False))

        (self.feed('res4b5_relu', 
                   'bn4b6_branch2c')
             .add(name='res4b6')
             .relu(name='res4b6_relu')
             .conv(1, 1, 256, 1, 1, biased=False, relu=False, name='res4b7_branch2a')
             .batch_normalization(relu=True, name='bn4b7_branch2a',is_training=False)
             .conv(3, 3, 256, 1, 1, biased=False, relu=False, name='res4b7_branch2b')
             .batch_normalization(relu=True, name='bn4b7_branch2b',is_training=False)
             .conv(1, 1, 1024, 1, 1, biased=False, relu=False, name='res4b7_branch2c')
             .batch_normalization(name='bn4b7_branch2c',is_training=False,relu=False))

        (self.feed('res4b6_relu', 
                   'bn4b7_branch2c')
             .add(name='res4b7')
             .relu(name='res4b7_relu')
             .conv(1, 1, 256, 1, 1, biased=False, relu=False, name='res4b8_branch2a')
             .batch_normalization(relu=True, name='bn4b8_branch2a',is_training=False)
             .conv(3, 3, 256, 1, 1, biased=False, relu=False, name='res4b8_branch2b')
             .batch_normalization(relu=True, name='bn4b8_branch2b',is_training=False)
             .conv(1, 1, 1024, 1, 1, biased=False, relu=False, name='res4b8_branch2c')
             .batch_normalization(name='bn4b8_branch2c',is_training=False,relu=False))

        (self.feed('res4b7_relu', 
                   'bn4b8_branch2c')
             .add(name='res4b8')
             .relu(name='res4b8_relu')
             .conv(1, 1, 256, 1, 1, biased=False, relu=False, name='res4b9_branch2a')
             .batch_normalization(relu=True, name='bn4b9_branch2a',is_training=False)
             .conv(3, 3, 256, 1, 1, biased=False, relu=False, name='res4b9_branch2b')
             .batch_normalization(relu=True, name='bn4b9_branch2b',is_training=False)
             .conv(1, 1, 1024, 1, 1, biased=False, relu=False, name='res4b9_branch2c')
             .batch_normalization(name='bn4b9_branch2c',is_training=False,relu=False))

        (self.feed('res4b8_relu', 
                   'bn4b9_branch2c')
             .add(name='res4b9')
             .relu(name='res4b9_relu')
             .conv(1, 1, 256, 1, 1, biased=False, relu=False, name='res4b10_branch2a')
             .batch_normalization(relu=True, name='bn4b10_branch2a',is_training=False)
             .conv(3, 3, 256, 1, 1, biased=False, relu=False, name='res4b10_branch2b')
             .batch_normalization(relu=True, name='bn4b10_branch2b',is_training=False)
             .conv(1, 1, 1024, 1, 1, biased=False, relu=False, name='res4b10_branch2c')
             .batch_normalization(name='bn4b10_branch2c',is_training=False,relu=False))

        (self.feed('res4b9_relu', 
                   'bn4b10_branch2c')
             .add(name='res4b10')
             .relu(name='res4b10_relu')
             .conv(1, 1, 256, 1, 1, biased=False, relu=False, name='res4b11_branch2a')
             .batch_normalization(relu=True, name='bn4b11_branch2a',is_training=False)
             .conv(3, 3, 256, 1, 1, biased=False, relu=False, name='res4b11_branch2b')
             .batch_normalization(relu=True, name='bn4b11_branch2b',is_training=False)
             .conv(1, 1, 1024, 1, 1, biased=False, relu=False, name='res4b11_branch2c')
             .batch_normalization(name='bn4b11_branch2c',is_training=False,relu=False))

        (self.feed('res4b10_relu', 
                   'bn4b11_branch2c')
             .add(name='res4b11')
             .relu(name='res4b11_relu')
             .conv(1, 1, 256, 1, 1, biased=False, relu=False, name='res4b12_branch2a')
             .batch_normalization(relu=True, name='bn4b12_branch2a',is_training=False)
             .conv(3, 3, 256, 1, 1, biased=False, relu=False, name='res4b12_branch2b')
             .batch_normalization(relu=True, name='bn4b12_branch2b',is_training=False)
             .conv(1, 1, 1024, 1, 1, biased=False, relu=False, name='res4b12_branch2c')
             .batch_normalization(name='bn4b12_branch2c',is_training=False,relu=False))

        (self.feed('res4b11_relu', 
                   'bn4b12_branch2c')
             .add(name='res4b12')
             .relu(name='res4b12_relu')
             .conv(1, 1, 256, 1, 1, biased=False, relu=False, name='res4b13_branch2a')
             .batch_normalization(relu=True, name='bn4b13_branch2a',is_training=False)
             .conv(3, 3, 256, 1, 1, biased=False, relu=False, name='res4b13_branch2b')
             .batch_normalization(relu=True, name='bn4b13_branch2b',is_training=False)
             .conv(1, 1, 1024, 1, 1, biased=False, relu=False, name='res4b13_branch2c')
             .batch_normalization(name='bn4b13_branch2c',is_training=False,relu=False))

        (self.feed('res4b12_relu', 
                   'bn4b13_branch2c')
             .add(name='res4b13')
             .relu(name='res4b13_relu')
             .conv(1, 1, 256, 1, 1, biased=False, relu=False, name='res4b14_branch2a')
             .batch_normalization(relu=True, name='bn4b14_branch2a',is_training=False)
             .conv(3, 3, 256, 1, 1, biased=False, relu=False, name='res4b14_branch2b')
             .batch_normalization(relu=True, name='bn4b14_branch2b',is_training=False)
             .conv(1, 1, 1024, 1, 1, biased=False, relu=False, name='res4b14_branch2c')
             .batch_normalization(name='bn4b14_branch2c',is_training=False,relu=False))

        (self.feed('res4b13_relu', 
                   'bn4b14_branch2c')
             .add(name='res4b14')
             .relu(name='res4b14_relu')
             .conv(1, 1, 256, 1, 1, biased=False, relu=False, name='res4b15_branch2a')
             .batch_normalization(relu=True, name='bn4b15_branch2a',is_training=False)
             .conv(3, 3, 256, 1, 1, biased=False, relu=False, name='res4b15_branch2b')
             .batch_normalization(relu=True, name='bn4b15_branch2b',is_training=False)
             .conv(1, 1, 1024, 1, 1, biased=False, relu=False, name='res4b15_branch2c')
             .batch_normalization(name='bn4b15_branch2c',is_training=False,relu=False))

        (self.feed('res4b14_relu', 
                   'bn4b15_branch2c')
             .add(name='res4b15')
             .relu(name='res4b15_relu')
             .conv(1, 1, 256, 1, 1, biased=False, relu=False, name='res4b16_branch2a')
             .batch_normalization(relu=True, name='bn4b16_branch2a',is_training=False)
             .conv(3, 3, 256, 1, 1, biased=False, relu=False, name='res4b16_branch2b')
             .batch_normalization(relu=True, name='bn4b16_branch2b',is_training=False)
             .conv(1, 1, 1024, 1, 1, biased=False, relu=False, name='res4b16_branch2c')
             .batch_normalization(name='bn4b16_branch2c',is_training=False,relu=False))

        (self.feed('res4b15_relu', 
                   'bn4b16_branch2c')
             .add(name='res4b16')
             .relu(name='res4b16_relu')
             .conv(1, 1, 256, 1, 1, biased=False, relu=False, name='res4b17_branch2a')
             .batch_normalization(relu=True, name='bn4b17_branch2a',is_training=False)
             .conv(3, 3, 256, 1, 1, biased=False, relu=False, name='res4b17_branch2b')
             .batch_normalization(relu=True, name='bn4b17_branch2b',is_training=False)
             .conv(1, 1, 1024, 1, 1, biased=False, relu=False, name='res4b17_branch2c')
             .batch_normalization(name='bn4b17_branch2c',is_training=False,relu=False))

        (self.feed('res4b16_relu', 
                   'bn4b17_branch2c')
             .add(name='res4b17')
             .relu(name='res4b17_relu')
             .conv(1, 1, 256, 1, 1, biased=False, relu=False, name='res4b18_branch2a')
             .batch_normalization(relu=True, name='bn4b18_branch2a',is_training=False)
             .conv(3, 3, 256, 1, 1, biased=False, relu=False, name='res4b18_branch2b')
             .batch_normalization(relu=True, name='bn4b18_branch2b',is_training=False)
             .conv(1, 1, 1024, 1, 1, biased=False, relu=False, name='res4b18_branch2c')
             .batch_normalization(name='bn4b18_branch2c',is_training=False,relu=False))

        (self.feed('res4b17_relu', 
                   'bn4b18_branch2c')
             .add(name='res4b18')
             .relu(name='res4b18_relu')
             .conv(1, 1, 256, 1, 1, biased=False, relu=False, name='res4b19_branch2a')
             .batch_normalization(relu=True, name='bn4b19_branch2a',is_training=False)
             .conv(3, 3, 256, 1, 1, biased=False, relu=False, name='res4b19_branch2b')
             .batch_normalization(relu=True, name='bn4b19_branch2b',is_training=False)
             .conv(1, 1, 1024, 1, 1, biased=False, relu=False, name='res4b19_branch2c')
             .batch_normalization(name='bn4b19_branch2c',is_training=False,relu=False))

        (self.feed('res4b18_relu', 
                   'bn4b19_branch2c')
             .add(name='res4b19')
             .relu(name='res4b19_relu')
             .conv(1, 1, 256, 1, 1, biased=False, relu=False, name='res4b20_branch2a')
             .batch_normalization(relu=True, name='bn4b20_branch2a',is_training=False)
             .conv(3, 3, 256, 1, 1, biased=False, relu=False, name='res4b20_branch2b')
             .batch_normalization(relu=True, name='bn4b20_branch2b',is_training=False)
             .conv(1, 1, 1024, 1, 1, biased=False, relu=False, name='res4b20_branch2c')
             .batch_normalization(name='bn4b20_branch2c',is_training=False,relu=False))

        (self.feed('res4b19_relu', 
                   'bn4b20_branch2c')
             .add(name='res4b20')
             .relu(name='res4b20_relu')
             .conv(1, 1, 256, 1, 1, biased=False, relu=False, name='res4b21_branch2a')
             .batch_normalization(relu=True, name='bn4b21_branch2a',is_training=False)
             .conv(3, 3, 256, 1, 1, biased=False, relu=False, name='res4b21_branch2b')
             .batch_normalization(relu=True, name='bn4b21_branch2b',is_training=False)
             .conv(1, 1, 1024, 1, 1, biased=False, relu=False, name='res4b21_branch2c')
             .batch_normalization(name='bn4b21_branch2c',is_training=False,relu=False))

        (self.feed('res4b20_relu', 
                   'bn4b21_branch2c')
             .add(name='res4b21')
             .relu(name='res4b21_relu')
             .conv(1, 1, 256, 1, 1, biased=False, relu=False, name='res4b22_branch2a')
             .batch_normalization(relu=True, name='bn4b22_branch2a',is_training=False)
             .conv(3, 3, 256, 1, 1, biased=False, relu=False, name='res4b22_branch2b')
             .batch_normalization(relu=True, name='bn4b22_branch2b',is_training=False)
             .conv(1, 1, 1024, 1, 1, biased=False, relu=False, name='res4b22_branch2c')
             .batch_normalization(name='bn4b22_branch2c',is_training=False,relu=False))

        (self.feed('res4b21_relu', 
                   'bn4b22_branch2c')
             .add(name='res4b22')
             .relu(name='res4b22_relu'))

        #========= RPN ============
        (self.feed('res4b22_relu')
             .conv(3,3,512,1,1,name='rpn_conv/3x3')
             .conv(1,1,len(anchor_scales)*3*2 ,1 , 1, padding='VALID', relu = False, name='rpn_cls_score'))
  

        (self.feed('rpn_conv/3x3')
             .conv(1,1,len(anchor_scales)*3*4, 1, 1, padding='VALID', relu = False, name='rpn_bbox_pred'))

        #========= RoI Proposal ============
        (self.feed('rpn_cls_score')
             .spatial_reshape_layer(2, name = 'rpn_cls_score_reshape')
             .spatial_softmax(name='rpn_cls_prob'))

        (self.feed('rpn_cls_prob')
             .spatial_reshape_layer(len(anchor_scales)*3*2, name = 'rpn_cls_prob_reshape'))

        (self.feed('rpn_cls_prob_reshape','rpn_bbox_pred','im_info')
             .proposal_layer(_feat_stride, anchor_scales, 'TEST',name = 'rois'))



        #========= RCNN ============ 
        (self.feed('res4b22_relu','rois')
             .roi_pool(7,7,1.0/16,name='res5a_branch2a_roipooling')
             .conv(1, 1, 512, 2, 2, biased=False, relu=False, name='res5a_branch2a',padding='VALID')
             .batch_normalization(relu=True, name='bn5a_branch2a',is_training=False)
             .conv(3, 3, 512, 1, 1, biased=False, relu=False, name='res5a_branch2b')
             .batch_normalization(relu=True, name='bn5a_branch2b',is_training=False)
             .conv(1, 1, 2048, 1, 1, biased=False, relu=False, name='res5a_branch2c')
             .batch_normalization(name='bn5a_branch2c',is_training=False,relu=False))

        (self.feed('res5a_branch2a_roipooling')
             .conv(1, 1, 2048, 2, 2, biased=False, relu=False, name='res5a_branch1',padding='VALID')
             .batch_normalization(name='bn5a_branch1',is_training=False,relu=False))


        (self.feed('bn5a_branch1', 
                   'bn5a_branch2c')
             .add(name='res5a')
             .relu(name='res5a_relu')
             .conv(1, 1, 512, 1, 1, biased=False, relu=False, name='res5b_branch2a')
             .batch_normalization(relu=True, name='bn5b_branch2a',is_training=False)
             .conv(3, 3, 512, 1, 1, biased=False, relu=False, name='res5b_branch2b')
             .batch_normalization(relu=True, name='bn5b_branch2b',is_training=False)
             .conv(1, 1, 2048, 1, 1, biased=False, relu=False, name='res5b_branch2c')
             .batch_normalization(name='bn5b_branch2c',is_training=False,relu=False))

        (self.feed('res5a_relu', 
                   'bn5b_branch2c')
             .add(name='res5b')
             .relu(name='res5b_relu')
             .conv(1, 1, 512, 1, 1, biased=False, relu=False, name='res5c_branch2a')
             .batch_normalization(relu=True, name='bn5c_branch2a',is_training=False)
             .conv(3, 3, 512, 1, 1, biased=False, relu=False, name='res5c_branch2b')
             .batch_normalization(relu=True, name='bn5c_branch2b',is_training=False)
             .conv(1, 1, 2048, 1, 1, biased=False, relu=False, name='res5c_branch2c')
             .batch_normalization(name='bn5c_branch2c',is_training=False,relu=False))

        (self.feed('res5b_relu', 
                   'bn5c_branch2c')
             .add(name='res5c')
             .relu(name='res5c_relu')
             .avg_pool(4, 4, 1, 1, padding='VALID', name='pool5')
             .fc(1000, relu=False, name='fc1000')
             .softmax(name='prob'))